Automatically developing neuropsychiatric treatment plans based on neuroimage data

ABSTRACT

A computer implemented method, apparatus, and computer program product for developing neuropsychiatric treatment plans. A treatment plan generator receives a set of diagnoses for a patient. The treatment plan generator automatically analyzes medical information in a set of electronic medical literature sources for potential therapies associated with treatment of each identified condition in the set of diagnoses. The treatment plan generator identifies the potential therapies associated with the treatment of each diagnosed condition. The treatment plan generator selects a set of recommended therapies from the potential therapies based on portions of the medical literature describing each therapy in the potential therapies and a medical history for the patient. The treatment plan generator generates a treatment plan. The treatment plan comprises the set of recommended therapies to treat each diagnosed condition in the set of diagnoses.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention is related generally to a data processing systemand in particular to a method and apparatus for generating treatmentplans. More particularly, the present invention is directed to acomputer implemented method, apparatus, and computer usable program codefor automatically generating neuropsychiatric treatment plans based onanalysis of neuroimage scans and medical literature.

2. Description of the Related Art

Neuropsychiatric conditions typically have neurological featuresassociated with disorders of the nervous system, as well as psychiatricfeatures. Neuropsychiatric conditions may be treated using a variety oftherapies, such as talk therapy, behavioral therapy, chemical therapy,and/or mechanical therapy. Chemical therapy refers to pharmacotherapy,such as, the utilization of drugs. Mechanical therapy includeselectroconvulsive therapies (ECT). These therapies may be usedseparately or may be used in combination to treat patients.

However, some patients may not receive the most effective treatmentsavailable due to difficulties in accurately diagnosing patients withneuropsychiatric conditions. In addition, patients that are accuratelydiagnosed may suffer from the negative side effects of effectivetherapies and/or trails of ineffective therapies. Furthermore, somepatients may suffer for years as a result of poorly understood diseasephenotype, particularly in cases involving the presentation of complexcases and/or multiple conditions in a single patient. In addition, whena disease is developing in a patient and the patient has not had asufficient number of “episodes” for diagnosis, or has only manifested afew early stage symptoms, it may be difficult or impossible to clearlyand rapidly delineate a differential diagnosis.

BRIEF SUMMARY OF THE INVENTION

According to one embodiment of the present invention, a computerimplemented method, apparatus, and computer program product is providedfor developing neuropsychiatric treatment plans. A treatment plangenerator receives a set of diagnoses for a patient. The treatment plangenerator automatically analyzes medical information in a set ofelectronic medical literature sources for potential therapies associatedwith treatment of each identified condition in the set of diagnoses. Thetreatment plan generator identifies the potential therapies associatedwith the treatment of each diagnosed condition. The treatment plangenerator selects a set of recommended therapies from the potentialtherapies based on portions of the medical literature describing eachtherapy in the potential therapies and a medical history for thepatient. The treatment plan generator generates a treatment plancomprising the set of recommended therapies to treat each diagnosedcondition in the set of diagnoses.

The treatment plan generator presents the treatment plan to a user witha link to relevant portions of the medical literature associated witheach therapy in the treatment plan. The treatment plan optionallycomprises an identification of negative drug interactions, side effects,allergic reactions, and negative effects on pre-existing conditions ofthe patient that are associated with the set of recommended therapies.In another embodiment, the treatment plan comprises a plurality oftherapies recommended for treatment of each neuropsychiatric conditionidentified in the set of diagnoses.

In one embodiment, the treatment plan generator identifies any therapyin the potential therapies that cannot be applied in conjunction with atleast one other therapy in the potential therapies to form aninapplicable therapy and removes the inapplicable therapy from the setof potential therapies. The treatment plan generator may also receivethe medical history, which comprises an identification of allergens ofthe patient. An allergen is a substance that has triggered an allergicreaction in the patient. The treatment plan generator identifies anytherapy associated with an allergen and removes the identified therapyfrom the set of recommended therapies.

In another embodiment, the treatment plan generator receives informationdescribing drug interactions and a list of drugs currently being takenby the patient. The treatment plan generator identifies a therapy in thepotential therapies that includes utilization of a drug that produces anegative drug interaction with a drug on the list of drugs currentlybeing taken by the patient and automatically removes the therapy fromthe set of recommended therapies. In yet another embodiment, thetreatment plan generator identifies a therapy that may be a cause of anegative impact on a pre-existing condition of the patient andautomatically removes the therapy from the set of recommended therapies.

In yet another embodiment, a neuroimage mapping manager receives a setof scans for the patient and automatically analyzes the set of scans toidentify regions of interest. The neuroimage mapping manager identifiesbrain architecture and brain metabolism based on characteristics in theregions of interest and generates the set of diagnoses based on thecharacteristics in the regions of interest, the brain architecture, andthe brain metabolism.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

FIG. 1 is a pictorial representation of a network of data processingsystems in which illustrative embodiments may be implemented;

FIG. 2 is a block diagram of a data processing system in whichillustrative embodiments may be implemented;

FIGS. 3A and 3B is a block diagram of a neuroimage mapping manager inaccordance with an illustrative embodiment;

FIG. 4 is a block diagram of a magnetic resonance imaging brain scanhaving regions of interest in accordance with an illustrativeembodiment;

FIG. 5 is a positron emissions tomography brain scan having regions ofinterest in accordance with an illustrative embodiment;

FIG. 6 is a flowchart illustrating a process for analyzing a set ofbrain scans for a patient to identify regions of interest with links torelevant portions of the medical literature in accordance with anillustrative embodiment;

FIG. 7 is a flowchart illustrating a process for generating baselinecontrol scans in accordance with an illustrative embodiment; and

FIG. 8 is a flowchart illustrating a process for automaticallygenerating a treatment plan in accordance with an illustrativeembodiment.

DETAILED DESCRIPTION OF THE INVENTION

As will be appreciated by one skilled in the art, the present inventionmay be embodied as a system, method or computer program product.Accordingly, the present invention may take the form of an entirelyhardware embodiment, an entirely software embodiment (includingfirmware, resident software, micro-code, etc.) or an embodimentcombining software and hardware aspects that may all generally bereferred to herein as a “circuit,” “module” or “system.” Furthermore,the present invention may take the form of a computer program productembodied in any tangible medium of expression having computer usableprogram code embodied in the medium.

Any combination of one or more computer usable or computer readablemedium(s) may be utilized. The computer-usable or computer-readablemedium may be, for example but not limited to, an electronic, magnetic,optical, electromagnetic, infrared, or semiconductor system, apparatus,device, or propagation medium. More specific examples (a non-exhaustivelist) of the computer-readable medium would include the following: anelectrical connection having one or more wires, a portable computerdiskette, a hard disk, a random access memory (RAM), a read-only memory(ROM), an erasable programmable read-only memory (EPROM or Flashmemory), an optical fiber, a portable compact disc read-only memory(CDROM), an optical storage device, a transmission media such as thosesupporting the Internet or an intranet, or a magnetic storage device.Note that the computer-usable or computer-readable medium could even bepaper or another suitable medium upon which the program is printed, asthe program can be electronically captured, via, for instance, opticalscanning of the paper or other medium, then compiled, interpreted, orotherwise processed in a suitable manner, if necessary, and then storedin a computer memory. In the context of this document, a computer-usableor computer-readable medium may be any medium that can contain, store,communicate, propagate, or transport the program for use by or inconnection with the instruction execution system, apparatus, or device.The computer-usable medium may include a propagated data signal with thecomputer-usable program code embodied therewith, either in baseband oras part of a carrier wave. The computer usable program code may betransmitted using any appropriate medium, including but not limited towireless, wireline, optical fiber cable, RF, etc.

Computer program code for carrying out operations of the presentinvention may be written in any combination of one or more programminglanguages, including an object oriented programming language such asJava, Smalltalk, C++ or the like and conventional procedural programminglanguages, such as the “C” programming language or similar programminglanguages. The program code may execute entirely on the user's computer,partly on the user's computer, as a stand-alone software package, partlyon the user's computer and partly on a remote computer or entirely onthe remote computer or server. In the latter scenario, the remotecomputer may be connected to the user's computer through any type ofnetwork, including a local area network (LAN) or a wide area network(WAN), or the connection may be made to an external computer (forexample, through the Internet using an Internet Service Provider).

The present invention is described below with reference to flowchartillustrations and/or block diagrams of methods, apparatus (systems) andcomputer program products according to embodiments of the invention. Itwill be understood that each block of the flowchart illustrations and/orblock diagrams, and combinations of blocks in the flowchartillustrations and/or block diagrams, can be implemented by computerprogram instructions.

These computer program instructions may be provided to a processor of ageneral purpose computer, special purpose computer, or otherprogrammable data processing apparatus to produce a machine, such thatthe instructions, which execute via the processor of the computer orother programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer program instructions may also bestored in a computer-readable medium that can direct a computer or otherprogrammable data processing apparatus to function in a particularmanner, such that the instructions stored in the computer-readablemedium produce an article of manufacture including instruction meanswhich implement the function/act specified in the flowchart and/or blockdiagram block or blocks.

The computer program instructions may also be loaded onto a computer orother programmable data processing apparatus to cause a series ofoperational steps to be performed on the computer or other programmableapparatus to produce a computer implemented process such that theinstructions which execute on the computer or other programmableapparatus provide processes for implementing the functions/actsspecified in the flowchart and/or block diagram block or blocks.

With reference now to the figures and in particular with reference toFIGS. 1-2, exemplary diagrams of data processing environments areprovided in which illustrative embodiments may be implemented. It shouldbe appreciated that FIGS. 1-2 are only exemplary and are not intended toassert or imply any limitation with regard to the environments in whichdifferent embodiments may be implemented. Many modifications to thedepicted environments may be made.

FIG. 1 depicts a pictorial representation of a network of dataprocessing systems in which illustrative embodiments may be implemented.Network data processing system 100 is a network of computers in whichthe illustrative embodiments may be implemented. Network data processingsystem 100 contains network 102, which is the medium used to providecommunications links between various devices and computers connectedtogether within network data processing system 100. Network 102 mayinclude connections, such as wire, wireless communication links, orfiber optic cables.

In the depicted example, server 104 and server 106 connect to network102 along with storage unit 108. In addition, clients 110, 112, and 114connect to network 102. Clients 110, 112, and 114 may be, for example,personal computers or network computers. In the depicted example, server104 provides data, such as boot files, operating system images, andapplications to clients 110, 112, and 114. Clients 110, 112, and 114 areclients to server 104 in this example. Network data processing system100 may include additional servers, clients, and other devices notshown.

In the depicted example, network data processing system 100 is theInternet with network 102 representing a worldwide collection ofnetworks and gateways that use the Transmission ControlProtocol/Internet Protocol (TCP/IP) suite of protocols to communicatewith one another. At the heart of the Internet is a backbone ofhigh-speed data communication lines between major nodes or hostcomputers, consisting of thousands of commercial, governmental,educational and other computer systems that route data and messages. Ofcourse, network data processing system 100 also may be implemented as anumber of different types of networks, such as for example, an intranet,a local area network (LAN), or a wide area network (WAN). FIG. 1 isintended as an example, and not as an architectural limitation for thedifferent illustrative embodiments.

With reference now to FIG. 2, a block diagram of a data processingsystem is shown in which illustrative embodiments may be implemented.Data processing system 200 is an example of a computer, such as server104 or client 110 in FIG. 1, in which computer usable program code orinstructions implementing the processes may be located for theillustrative embodiments. In this illustrative example, data processingsystem 200 includes communications fabric 202, which providescommunications between processor unit 204, memory 206, persistentstorage 208, communications unit 210, input/output (I/O) unit 212, anddisplay 214.

Processor unit 204 serves to execute instructions for software that maybe loaded into memory 206. Processor unit 204 may be a set of one ormore processors or may be a multi-processor core, depending on theparticular implementation. Further, processor unit 204 may beimplemented using one or more heterogeneous processor systems in which amain processor is present with secondary processors on a single chip. Asanother illustrative example, processor unit 204 may be a symmetricmulti-processor system containing multiple processors of the same type.

Memory 206 and persistent storage 208 are examples of storage devices. Astorage device is any piece of hardware that is capable of storinginformation either on a temporary basis and/or a permanent basis. Memory206, in these examples, may be, for example, a random access memory orany other suitable volatile or non-volatile storage device. Persistentstorage 208 may take various forms depending on the particularimplementation. For example, persistent storage 208 may contain one ormore components or devices. For example, persistent storage 208 may be ahard drive, a flash memory, a rewritable optical disk, a rewritablemagnetic tape, or some combination of the above. The media used bypersistent storage 208 also may be removable. For example, a removablehard drive may be used for persistent storage 208.

Communications unit 210, in these examples, provides for communicationswith other data processing systems or devices. In these examples,communications unit 210 is a network interface card. Communications unit210 may provide communications through the use of either or bothphysical and wireless communications links.

Input/output unit 212 allows for input and output of data with otherdevices that may be connected to data processing system 200. Forexample, input/output unit 212 may provide a connection for user inputthrough a keyboard and mouse. Further, input/output unit 212 may sendoutput to a printer. Display 214 provides a mechanism to displayinformation to a user.

Instructions for the operating system and applications or programs arelocated on persistent storage 208. These instructions may be loaded intomemory 206 for execution by processor unit 204. The processes of thedifferent embodiments may be performed by processor unit 204 usingcomputer implemented instructions, which may be located in a memory,such as memory 206. These instructions are referred to as program code,computer usable program code, or computer readable program code that maybe read and executed by a processor in processor unit 204. The programcode in the different embodiments may be embodied on different physicalor tangible computer readable media, such as memory 206 or persistentstorage 208.

Program code 216 is located in a functional form on computer readablemedia 218 that is selectively removable and may be loaded onto ortransferred to data processing system 200 for execution by processorunit 204. Program code 216 and computer readable media 218 form computerprogram product 220 in these examples. In one example, computer readablemedia 218 may be in a tangible form, such as, for example, an optical ormagnetic disc that is inserted or placed into a drive or other devicethat is part of persistent storage 208 for transfer onto a storagedevice, such as a hard drive that is part of persistent storage 208. Ina tangible form, computer readable media 218 also may take the form of apersistent storage, such as a hard drive, a thumb drive, or a flashmemory that is connected to data processing system 200. The tangibleform of computer readable media 218 is also referred to as computerrecordable storage media. In some instances, computer recordable media218 may not be removable.

Alternatively, program code 216 may be transferred to data processingsystem 200 from computer readable media 218 through a communicationslink to communications unit 210 and/or through a connection toinput/output unit 212. The communications link and/or the connection maybe physical or wireless in the illustrative examples. The computerreadable media also may take the form of non-tangible media, such ascommunications links or wireless transmissions containing the programcode.

The different components illustrated for data processing system 200 arenot meant to provide architectural limitations to the manner in whichdifferent embodiments may be implemented. The different illustrativeembodiments may be implemented in a data processing system includingcomponents in addition to or in place of those illustrated for dataprocessing system 200. Other components shown in FIG. 2 can be variedfrom the illustrative examples shown.

As one example, a storage device in data processing system 200 is anyhardware apparatus that may store data. Memory 206, persistent storage208, and computer readable media 218 are examples of storage devices ina tangible form.

In another example, a bus system may be used to implement communicationsfabric 202 and may be comprised of one or more buses, such as a systembus or an input/output bus. Of course, the bus system may be implementedusing any suitable type of architecture that provides for a transfer ofdata between different components or devices attached to the bus system.Additionally, a communications unit may include one or more devices usedto transmit and receive data, such as a modem or a network adapter.Further, a memory may be, for example, memory 206 or a cache such asfound in an interface and memory controller hub that may be present incommunications fabric 202.

The illustrative embodiments recognize that it is sometimes difficultfor medical practitioners to develop an effective treatment plan incomplex cases involving multiple neuropsychiatric conditions, druginteractions, pre-existing conditions that may be impacted bytreatments, allergies, new therapies, side-effects, interactions ofdifferent therapies when applied together to treat a patient, newmedical study results, and other factors that may influence selection oftreatments to be used on a particular patient. Moreover, gathering themost up-to-date medical information associated with each therapy that isavailable so that the medical practitioner can make the best informedtreatment decisions may be time consuming and burdensome on the medicalpractitioners.

According to one embodiment of the present invention, a computerimplemented method, apparatus, and computer program product is providedfor developing neuropsychiatric treatment plans. A treatment plangenerator receives a set of diagnoses for a patient. As used herein, theterm “set” refers to one or more, unless indicated otherwise. Thus, inthis embodiment, the set of diagnoses may include a single diagnosis ofa single neuropsychiatric condition, as well as two or more diagnosesfor two or more different neuropsychiatric conditions.

The treatment plan generator automatically analyzes medical informationfrom a set of electronic medical literature sources to identify forpotential therapies associated with treatment of each identifiedcondition in the set of diagnoses. A potential therapy is a known and/oravailable therapy that may be used to treat a particular condition. Atherapy is not required to cure the condition. A therapy may be a cureor a therapy may only be a treatment intended to improve the quality oflife of a patient, reduce appearance of symptoms, reduce the severity ofsymptoms, reduce the frequency of occurrence of symptoms, improve thefunctionality of the patient, reduce patient discomfort, or otherwiseprovide a benefit to the patient.

The treatment plan generator identifies the potential therapiesassociated with the treatment of each diagnosed condition. The treatmentplan generator selects a set of recommended therapies from the potentialtherapies based on portions of the medical literature describing eachtherapy in the potential therapies and a medical history for thepatient. The set of recommended therapies may be a single therapy, aswell as two or more different therapies. A therapy in the set ofrecommended therapies may include, without limitation, talk therapy,behavioral therapy, chemical therapy, and/or mechanical therapy. The setof recommended therapies may include a single therapy for eachneuropsychiatric condition in the set of diagnoses. The set ofrecommended therapies may also include multiple therapies for a singleneuropsychiatric condition identified in the set of diagnose. The set ofrecommended therapies may also include two or more recommended therapiesfor each neuropsychiatric condition identified in the set of diagnoses.The set of recommended therapies may also include a recommendation thatno therapies be applied to treat a particular identified condition inthe set of diagnoses. A recommendation of no therapy may be made if notherapies are available to treat the condition or if the therapy may bemore harmful than beneficial to the patient due to harmful side effects,negative impact on pre-existing conditions, harmful drug interactions,allergies, the patient's lack of response to a given therapy, or aprevious negative reaction to a given therapy.

In other words, if a given condition has only been successfully treatedwith a particular drug therapy and the drug therapy has knownside-effects that are likely to exacerbate a pre-existing condition, thedrug therapy may not be a viable option. For example, certainantidepressants have a negative drug interaction with Lithium.Therefore, if a patient is taking Lithium for a pre-existing condition,a therapy for a newly diagnosed condition that calls for utilization ofantidepressants known to have the negative drug interaction with Lithiumare not included in the treatment plan. If no other therapy isavailable, the best treatment for the newly diagnosed condition may beno treatment at all. However, if other treatments, such as talk therapymay be beneficial, the treatment plan generator may include talk therapyin the set of recommended therapies for the newly diagnosed condition.

The treatment plan generator generates a treatment plan using the set ofrecommended therapies to treat each diagnosed condition in the set ofdiagnoses. The treatment plan may include all the therapies in the setof recommended therapies or only a subset of the therapies in the set ofrecommended therapies.

The treatment plan generator presents the treatment plan to a user witha link to relevant portions of the medical literature associated witheach therapy in the treatment plan. A link may be a link to a source ona network, such as a hyperlink, or a link to a source on a local datastorage device. The treatment plan optionally comprises anidentification of negative drug interactions, side effects, allergicreactions, and negative effects on pre-existing conditions of thepatient that are associated with the set of recommended therapies. Inanother embodiment, the treatment plan comprises a plurality oftherapies recommended for treatment of each neuropsychiatric conditionidentified in the set of diagnoses.

In one embodiment, the selection of a set of recommended therapies fromthe potential therapies based on portions of the medical literaturedescribing each therapy in the potential therapies and a medical historyfor the patient further comprises identifying a therapy in the potentialtherapies that cannot be applied in conjunction with at least one othertherapy in the potential therapies to form an inapplicable therapy andremoving the inapplicable therapy from the set of potential therapies.The at least one other therapy may be a talk therapy, a mechanicaltherapy, a pharmacotherapy, any other type of therapy, or anycombination of these therapies. As used herein, the term “at least one”refers to a one, as well as two or more. Therefore, “at least one othertherapy” refers to one or more other therapies. The one or more othertherapies may be the same therapy or a combination of differenttherapies. For example, and without limitation, the at least one therapymay be a single drug therapy, two different drug therapies, or acombination of two drug therapies and talk therapy.

The selection of recommended therapies may also include the treatmentplan generator receiving the medical history. In this embodiment, themedical history comprises an identification of allergens of the patient.An allergen is a substance that has triggered an allergic reaction inthe patient. The treatment plan generator identifies any therapyassociated with an allergen and removes the identified therapy from theset of recommended therapies.

In another embodiment, the treatment plan generator receives informationdescribing drug interactions and a list of drugs currently being takenby the patient. The treatment plan generator identifies a therapy in thepotential therapies that includes utilization of a drug that produces anegative drug interaction with a drug on the list of drugs currentlybeing taken by the patient and automatically removes the therapy fromthe set of recommended therapies. In yet another embodiment, thetreatment plan generator identifies any therapy that may be a cause of anegative impact on a pre-existing condition of the patient andautomatically removes the therapy from the set of recommended therapies.

In yet another embodiment, a neuroimage mapping manager receives a setof scans for the patient and automatically analyzes the set of scans toidentify regions of interest. The neuroimage mapping manager identifiesbrain architecture and brain metabolism based on characteristics in theregions of interest and generates the set of diagnoses based on thecharacteristics in the regions of interest, the brain architecture, andthe brain metabolism.

FIGS. 3A and 3B is a block diagram of a neuroimage mapping manager inaccordance with an illustrative embodiment. Neuroimage mapping manager300 is software for analyzing patient brain scans to identify regions ofinterest in the brain scans and generate links to portions of themedical literature that is associated with the regions of interest.Computer 301 may be implemented in any type of computing device, suchas, without limitation, a server, a client, a laptop computer, apersonal digital assistant (PDA), a smart phone, or any other known oravailable computing device shown in FIG. 1 and FIG. 2.

Neuroimage analyzer 302 receives patient set of scans 304. Patient setof scans 304 is a set of one or more scans of a patient's brain. Patientset of scans 304 may include functional magnetic resonance imaging(fMRI) scans, structural magnetic resonance imaging (sMRI) scans,positron emission tomography (PET) scans, and/or any other type of brainscans. Patient set of scans 304 may include only positron emissiontomography scans, only magnetic resonance imaging scans, or acombination of positron emission tomography scans and magnetic resonanceimaging scans. As used herein, the term “patient” is not limited to apatient admitted in a hospital. The term “patient” may refer to anyperson obtaining medical care, consulting a medical practitioner,participating in a medical study, obtaining medical advice, or otherwiseparticipating in medical tests and/or medical procedures.

The scans in patient set of scans 304 may be generated by one or morescanning devices, such as scanning device 305. Scanning device 305 maybe implemented as one or more of a functional magnetic resonance imagingdevice, a structural magnetic resonance imaging device, a positronemission tomography device, or any other type of device for generatingscans of a human patient's brain. Scanning device 305 in this example isa single scanning device. However, scanning device 305 may also includetwo or more scanning devices. Scanning device 305 optionally saves thescans of the patient's brain in data storage device 306.

Data storage device 306 may be implemented as a hard drive, a flashmemory, a main memory, read only memory (ROM), a random access memory(RAM), or any other type of data storage device. Data storage may beimplemented in a single data storage device or a plurality of datastorage devices. Thus, neuroimage analyzer 302 may receive the scans inpatient set of scans 304 from scanning device 305 as each scan isgenerated, or neuroimage analyzer 302 may retrieve the scans from apre-generated set of scans stored in data storage device 306.

Comparator 307 is a software component that compares patient set ofscans 304 to baseline normal scans 308 and/or baseline abnormal scans310 to identify regions of interest 312. A region of interest is an areain a scan that shows an indication of a potential abnormality, apotential illness, a potential disease, a potential neuropsychiatriccondition, or any other deviation from what would be expected in a scanof the region for a healthy individual having similar characteristics asthe patient. The similar characteristics may include, withoutlimitation, an age range of the patient, gender, pre-existingconditions, profession, or other factors influencing the development,function, structure, and/or appearance of an area of the brain as shownin a scan.

Baseline normal scans 308 may include, without limitation, a set of oneor more brain scans for average, healthy subjects having one or morecharacteristics in common with the patient. The characteristics incommon may be, without limitation, age, age-range, gender, pre-existingconditions, profession, place of residence, nationality, and/or anyother characteristic. For example, if the patient is a sixteen year oldfemale, baseline normal scans 308 may include scans of normal, healthyfemale subjects between the ages of fourteen and eighteen. Comparator307 compares one or more areas in each scan in patient set of scans 304with corresponding areas in one or more scans in baseline normal scans308 to identify areas of the patient's scans that are consistent withthe scans of normal, healthy subjects and to identify areas of the scansthat are inconsistent with the scans of normal, healthy subjects. Anarea in a scan that is inconsistent with the corresponding areas inbaseline normal scans 308 are identified as a region of interest inregions of interest 312. A region identified in regions of interest 312may indicate a potential abnormality, illness, or condition. However,each region in regions of interest 312 are not required to definitivelyindicate an abnormality, illness, condition, or other deviation from thenorm.

Baseline abnormal scans 310 is a set of one or more scans of subjectshaving one or more characteristics in common with the patient anddiagnosed with an identified condition. The identified condition may bea disease, an illness, a deformity, a neuropsychiatric condition, anabnormality, or any other identified deviation from the norm. Forexample, if the patient is a male, age thirty five, and diagnosed withbipolar disorder, the baseline abnormal scans may include scans of malepatients between the ages of thirty and forty, diagnosed with bipolardisorder, and having a variety of known neuropsychiatric disorders.Comparator 307 compares regions in each scan in patient set of scans 304with one or more scans in baseline abnormal scans 310 to identifyregions of interest in the patient's scans that show indications ofdisorders, illness, disease, or abnormalities. A region in a scan mayshow indications of a potential illness, condition, abnormality, ordisorder if the region in the patient's scan is consistent with acorresponding region in a brain scan of one or more subjects having aknown illness, condition, abnormality, or neuropsychiatric disorder.Thus, neuroimage analyzer 302 analyzes patient set of scans 304 toidentify regions of interest in the scans based on baseline normal scansand/or baseline disorder scans for identified illnesses, abnormalities,diseases, disorders, or other known conditions.

Medical data and text analytics 314 is a software component forsearching set of electronic medical literature sources 316 for medicalliterature relevant to regions of interest 312 in patient set of scans304. Set of electronic medical literature sources 316 is a set of one ormore sources of medical literature 318. Set of electronic medicalliterature sources 316 may include both online medical literaturesources that are accessed by medical data and text analytics 314 via anetwork connection, as well as off-line medical literature sources thatmay be accessed without a network connection. Example of electronicmedical literature sources include, for example and without limitation,PUBMED, and a medical literature database. Medical literature 318 is anyliterature, journal article, medical study results, medical text,pharmaceutical studies, or any other medical information in anelectronic format. Medical literature 318 may include scans 320, such asmagnetic resonance imaging scans, positron emission tomography scans, orany other type of brain scans.

Medical data and text analytics 314 comprises search engine 322. Searchengine 322 is any type of known or available information retrievalsoftware for locating medical literature that is relevant to regions ofinterest 312 in set of electronic medical literature sources 316. Searchengine 322 may be software for searching data storage devices on acomputer system or a web search tool for searching for medicalinformation on the World Wide Web. Search engine 322 may also makequeries into databases, information systems, and other medicalliterature information sources to locate information relevant to regionsof interest 312.

Data mining 324 is a software tool for searching through informationavailable from one or more sources and retrieving medical informationrelevant to regions of interest 312. Data mining 324, search engine 322,or any other software for locating relevant information may be used tosearch set of electronic medical literature sources 316 for relevantmedical literature. Searching through the information from one or moresources may include, without limitation, using at least one of datamining, search engines, pattern recognition, queries to identify therelevant medical literature in the medical literature available from theset of electronic medical literature sources, data mining cohort,pattern recognition cohort, search engine cohort, or any other cohortappliance of interest.

A cohort is a group of one or more objects having a commoncharacteristic. For example, a data mining cohort may be, withoutlimitation, a group of one or more objects associated with performingdata mining techniques to identify desired data from a data source. Apattern recognition cohort may be, without limitation, a group ofpattern recognition software applications that identify patterns indata, such as medical data.

Parser 326 is software for parsing medical literature 318 text into aform suitable for further analysis and processing. Parser 326 may beimplemented as any type of known or available parser. Correlation engine328 correlates portions of medical literature 318 with regions ofinterest 312 to form portions of medical literature 330 that arerelevant or associated with regions of interest 312. A portion ofmedical literature that is relevant or associated with a region ofinterest is a section of medical literature text and/or one or morescans that describes a region of interest, describes a condition,illness, deformity, abnormality, disease, or other cause for anappearance of a region of interest, an area in a scan that is the sameor similar to an area of interest, an area in a scan in scans 320 or aportion of text in a medical literature document that is otherwiseassociated with a characteristic, feature, structure, indicator of brainchemistry, indicator of brain function, or other feature shown in anarea of interest in a patient's brain scan. A relevant portion ofmedical literature may also describe the appearance of the region ofinterest, describe causes for the appearance of the region of interest,and/or describe therapies used to treat patients having brain scans thatshowed similar features as the features shown in one or more region ofinterests in the patient's brain scan.

For example, if a region of interest in patient's brain scan indicatesan enlargement of a brain ventricle, a scan in scans 320 in medicalliterature 318 showing a similar enlargement of the brain ventricle is aportion of medical literature that is relevant or associated withregions of interest 312. Likewise, if a section of a medical journalarticle in an electronic format in medical literature 318 describesvarious causes of enlargement of a brain ventricle, that section of themedical literature is also relevant or associated with regions ofinterest 312. Thus, in this example, portions of medical literature 330include both the scan showing the enlargement of the ventricle in adifferent patient and the portion of the medical journal articlediscussing possible causes of an enlargement of the ventricles inpatients. Any portions of the medical literature discussing treatmentsused in patients having conditions that are associated with enlargedbrain ventricles may also be included.

In this manner, medical data and text analytics 314 is capable ofautomatically searching for electronic medical literature sources toidentify portions of the medical literature that are relevant to aparticular patient's diagnosis and/or treatment, and correlating eachitem, such as a scan or a section in a journal article, to each regionof interest in the patient's brain scans. When a user wishes to view allthe relevant medical literature associated with a particular region ofinterest, the user can simply request all the portions of medicalliterature correlated to the particular region of interest. In response,neuroimage mapping manager 300 only provides the portions of medicalliterature 318 from a plurality of medical literature sources that maybe useful to the user, rather than providing the full text of allmedical journal articles that have certain key words or search phrases,as is currently done.

Neuroimage mapping manager 300 may also generate result 332, including aset of links to portions of medical literature 330. Result 332 mayoptionally include an identification of a source and/or citation for thesource of each portion of medical literature. The set of links toportions of medical literature 330 may also optionally be embedded inpatient set of scans 304 and/or embedded within regions of interest 312in patient set of scans. The set of links to portions of medicalliterature 330 may also optionally be presented as a separate resultapart from patient set of scans 304 and/or apart from regions ofinterest 312.

In another embodiment, the set of links to portions of medicalliterature 330 are embedded in an electronic medical file for thepatient or a file for brain scan results for one or more patients. Auser selects a link in the set of links to view a portion of medicalliterature associated with a region of interest. In such a case, theportions of medical literature 330 in the patient's medical file mayinclude a set of links to patient set of scans 304 and/or a set of linksto regions of interest 312. In such a case, each portion of the medicalliterature, such as a scan or a section of a medical journal article,may include a link to the region of interest that is associated with orrelevant to that portion of the medical literature. Likewise, all theportions of the medical literature that are relevant to a particularregion of interest may include a single link to that particular regionof interest rather than each portion of the medical literature includinga separate link to the particular region of interest or regions ofinterest associated with the portions of the medical literature.

The portion of medical literature may be a scan only, text only, or acombination of text and one or more scans. The portion of medicalliterature may be an entire or complete item, such as a complete medicaljournal article or a complete section of a medical textbook, if theentire journal article or complete section of the medical text isrelevant to the features shown in a particular region of interest. Theportion of medical literature may also be a portion of a journalarticle, a portion of a section of a medical textbook, or other part ofan item of medical literature. In such a case, a user may optionallyselect to view the entire journal article or the entire medical textrather than viewing only the relevant portion of the journal article ormedical text.

In this embodiment, baseline normal scans 308 and baseline abnormalscans 310 are pre-generated and available for retrieval from datastorage device 306. However, in another embodiment, medical data andtext analytics 314 searches set of electronic medical literature sources316 for scans of normal, healthy subjects to create baseline normalscans 308. Medical data and text analytics 314 also searches set ofmedical literature sources 316 for scans of subjects having knownabnormalities, deformities, illnesses, ailments, diseases, or otherneuropsychiatric disorders to create baseline abnormal scans 310.

Thus, neuroimage mapping manager 300 provides data and text analytics toautomatically determine regions of a patient's brain affected byneuropsychiatric conditions and/or other illness or abnormality asdepicted in functional neuroimage data. Neuroimage data is dataassociated with a brain scan, such as functional magnetic resonanceimaging and positron emission tomography scans. Neuroimage mappingmanager 300 applies technologies to data, such as heuristics, whichautomatically correlate the features identified in regions of interest312 with relevant portions of medical literature 330 in medicalliterature 318 that describes regions of interest 312.

Input/output 334 may be implemented as any type of input and/or outputdevice for presenting output to a user and receiving input from a user.For example, input/output 334 may present regions of interest 312 to auser and/or receive a selection of one or more regions of interest froma user. Input/output 334 may also be used to present result 332,potential diagnoses, or other information to a user. Neuroimage analyzer302 may optionally present the automatically selected regions ofinterest to the user using input/output 334. The automatically selectedregions of interest may be presented using a display device to presentthe regions of interest in a visual format, using an audio device topresent the regions of interest to the user in an audio format, using atactile interface that may be read by the visually impaired, using acombination of audio and visual devices, using a combination of audioand tactile devices, or any other presentation device.

The user may utilize input/output 334 to choose to select one or moreadditional regions of interest in patient set of scans 304. In such acase, neuroimage analyzer 302 adds the manually selected set of one ormore regions of interest to regions of interest 312. In one embodiment,the regions of interest that are not automatically selected byneuroimage analyzer 302 and/or regions of interest that are not manuallyselected by the user are automatically removed by neuroimage analyzer302. In another embodiment, the user may choose to manually de-select orremove one or more regions of interest that was automatically selectedby neuroimage analyzer 302. In such a case, neuroimage analyzer 302automatically removes the one or more regions of interest selected forremoval by the user from regions of interest 312.

In another embodiment, neuroimage mapping manager 300 makes adetermination as to whether indicators correlate with the patient'sclinical data. Clinical data 336 is data describing the results ofclinical laboratory tests. Clinical data 336 may include, withoutlimitation, urinalysis tests, blood tests, thyroid tests, biopsyresults, cultures, electrolyte tests, genetic tests, bone marrow tests,tests for the presence of viral agents/illness, tests for the presenceof bacterial agents/illnesses, hormone tests, or any other type oflaboratory tests. Clinical data 336 describes the presence of substancesin the blood, urine, tissue, hormone levels, body chemistry, and bodyfluids. Clinical data 334 may be relevant to diagnosis or therapy for aparticular condition.

Moreover, clinical data 336 may reveal causes of one or more features inthe brain scans. For example, clinical tests may indicate mercurypoisoning or other substances in the blood that may be responsible forthe abnormal appearance of a region in a brain scan. Clinical data 336for a particular patient may be available on data storage device 306,obtained from a remote data storage device via a network connection,and/or may be manually input to neuroimage mapping manager throughinput/output device 334. If the features in a region of interestcorrelate with clinical data 336, neuroimage mapping manager 300identifies the correlations in result 332. The correlations may beprovided as links to information embedded within regions of interest 312or provided separately from regions of interest 312.

Patient medical history 338 is a record of the patient's past andcurrent medical treatments, prescribed drugs, medical procedures,diagnoses, treating physicians, known allergies, and/or any othermedical information associated with the patient. Neuroimage mappingmanager 300 may correlate information in patient medical history 338that may be responsible for an appearance or presence of a feature in aregion of interest with that particular region in regions of interest312.

For example, if patient medical history 338 indicates that the patientsuffered a head trauma in a car accident when the patient was a childthat led to structural damage in a particular area of the brain, thatinformation is linked to the region of interest corresponding to thearea of the brain where the effects of the head trauma are evident.Likewise, if the patient had brain surgery to prevent or lessen theeffects of seizures and the epilepsy surgery effects brain function inone or more areas of the brain, the regions of interest that arecorrelated to the areas of the brain effected by the epilepsy surgeryare identified in regions of interest 312 with a link to the portion ofthe patient medical history 338 discussing the epilepsy surgery andeffects of the epilepsy surgery.

Diagnostic engine 344 is software for analyzing regions of interest 312,portions of medical literature 330, clinical data 336, patient medicalhistory 338, as well as any other patient data to automatically identifyindicators of potential neuropsychiatric conditions. An indicatorincludes, without limitation, a symptom, a behavior, a test result, afeature of a brain scan, or any other indicator of a given condition.Indicators of neuropsychiatric conditions may include, for example, andwithout limitation, levels of brain metabolism, structural features ofthe brain, functional aspects of the brain, behavioral tics, levels ofchemicals, such as dopamine and neurotransmitters, and other indicatorsof neuropsychiatric conditions.

Diagnostic engine 344 compares the set of indicators of potentialneuropsychiatric conditions with diagnostic signatures. A diagnosticsignature is a signature that corresponds to at least one indicator inthe set of indicators. The diagnostic signatures are generatedautomatically by diagnostic engine 344 by analyzing medical literature318 and scans 320 of patients with known neuropsychiatric disorders toidentify indicators or signatures of each known neuropsychiatricdisorder. For example, and without limitation, if a majority of patientsdiagnosed with schizophrenia have enlarged brain ventricles, enlargedbrain ventricles are identified as a signature of schizophrenia.

Diagnostic engine 344 may also attach a weighting to each signature. Forexample, if a majority of patients diagnosed with schizophrenia have theenlarged brain ventricles but only thirty percent of patients diagnosedwith schizophrenia have a decreased level of metabolism in a particularregion of the brain, diagnostic engine 344 may assign a higher weightingto a signature of enlarged brain ventricles than a signature fordecreased metabolism in the particular region of the brain. Diagnosticengine 344 optionally uses a weighting to identify each diagnosis in setof diagnoses 346 for the patient. In another embodiment, a user maymanually input one or more diagnosis in set of diagnoses 346. In otherwords, rather than diagnostic engine 344 generating all the diagnosis inset of diagnoses 346, a user may manually provide one or more diagnosisby via input/output 334. A user may also optionally enter one or moresignatures for utilization by diagnostic engine 344. In other words,rather than diagnostic engine 344 automatically generating thesignatures, a user may manually provide one or more signatures and/orweighting for signatures for utilization by diagnostic engine 344 viainput/output 334.

Diagnostic engine 344 identifies diagnostic signatures that correspondto one or more indicators to form matching signatures. Diagnostic engine344 identifies a potential diagnosis associated with each matchingsignature to form set of diagnoses 346. Set of diagnoses 346 may includea single diagnosis of one condition, as well as diagnoses for two ormore neuropsychiatric conditions.

Set of diagnoses 346 may optionally include additional information usedby diagnostic engine 344 to generate each diagnosis. This additionalinformation is provided with the diagnoses in set of diagnoses 346 topermit a user to easily review information associated with thediagnoses. For example, and without limitation, set of diagnoses 346 mayinclude an identification of the indicators of neuropsychiatricconditions, an identification of the matching diagnostic signatures, theweighting assigned to each matching diagnostic signature, regions ofinterest 312, portions of medical literature 330, relevant portions ofclinical data 336, relevant portions of patient medical history 338, andany other information relevant to each diagnosis generated by diagnosticengine 344.

In another embodiment, set of diagnoses 346 includes a link to theadditional information. For example, and without limitation, set ofdiagnoses 346 may include a link to portions of medical literature 330,a link to regions of interest 312, a link to relevant portions ofclinical data 336, a link to relevant portions of patient medicalhistory 338, a link to matching diagnostic signatures and the weightingfor each matching diagnostic signature, as well as links to any otherrelevant data used to generate each diagnosis.

Treatment plan generator 348 is software for automatically generating atreatment plan for the patient based on set of diagnoses 346. Treatmentplan generator 348 analyzes relevant portions of medical literature 330for potential therapies to treat each condition identified in set ofdiagnoses 346. Treatment plan generator 348 identifies the potentialtherapies and selects one or more therapies from the potential therapiesthat are recommended based on the regions of interest 312, patientmedical history 338, clinical data 336, and any other patient data toform set of recommended therapies 350. Each therapy in the set ofrecommended therapies 350 may be associated with drug interactions, sideeffects, risks based on pre-existing conditions, ingredients that may beallergens, and other contraindications. A contraindication substance isa substance that could trigger an exacerbation of an underlyingcondition in the patient. For example, and without limitation, patientswith schizophrenia and certain bipolar disorders should not take certaintypes of allergy medications because the allergy medications mayprecipitate manic or psychotic symptoms. Likewise, certain decongestantsare contraindicated for bipolar patients with a history of mania.

Treatment plan generator 348 generates treatment plan 352 using set ofrecommended therapies 350. Treatment plan 352 may include a singletherapy or a plurality of therapies. In this example, and withoutlimitation, treatment plan 352 includes therapy A, therapy B, therapy C,and therapy D. However, treatment plan 352 may also include notherapies, a single therapy, two therapies, five therapies, or any othernumber of therapies.

In one embodiment, treatment plan generator 348 eliminates any therapiesfrom the set of recommended therapies 350 based on the druginteractions, side effects, risk, allergies, and othercontraindications. For example, if a therapy includes use of a drug thatincludes iodine and patient medical history 338 indicates that thepatient is allergic to iodine, treatment plan generator 348 eliminatesthe therapy from set of recommended therapies. Treatment plan generator348 generates treatment plan 352 without the therapies that areeliminated from set of recommended therapies 350. In another embodiment,rather than remove the contraindicated therapies from set of recommendedtherapies 350, treatment plan generator 348 includes informationdescribing the drug interaction, side effect, risk, potential allergicreaction, or other contraindications in treatment plan 352 for review bya user prior to implementation of treatment plan 352. In the exampleprovided above, rather than remove the therapy including iodine from thetreatment plan, the treatment plan includes a warning regarding thepotential for an allergic reaction due to the iodine ingredient and thepatient's medical history indicating an allergy to iodine.

In another embodiment, treatment plan 352 may also include relevantportions of the medical literature. The relevant portions of the medicalliterature may include, without limitation, information describing druginteraction warning, side-effects associated with each therapy, dosagerecommendations, contraindications, allergy warnings, and an expectedresponse to each therapy by the patient. Treatment plan 352 may alsoinclude recommendations as to when each therapy should be administered,a frequency with which each therapy should be administered, drugdosages, requirements for taking food with a drug therapy, fasting priorto administering a drug therapy, and other recommendations for applyingthe therapies to the patient.

In this embodiment, neuroimage mapping manager 300 and treatment plangenerator 348 are implemented on computer 301. However, treatment plangenerator 348 may also be implemented on different computing devices.For example, treatment plan generator 348 may be implemented on a remoteserver that receives regions of interest 312 and portions of medicalliterature 330 from neuroimage mapping manager 300 on computer 301 via anetwork connection, such as network 102 in FIG. 1.

Referring to FIG. 4, a block diagram of a magnetic resonance imagingbrain scan having regions of interest is depicted in accordance with anillustrative embodiment. Scan 400 is a positron emission tomography scanof a brain of a patient. Scan 402 is a positron emission tomography scanof a normal, healthy subject. Scan 400 has regions of interest 404-408.Regions of interest 404-408 are areas in scan 400 that show indicationsof one or more neuropsychiatric conditions, such as, for example andwithout limitation, schizophrenia. In this example, regions of interest404-408 show disruptions in brain activity. Region 406 shows abnormalchanges in the ventricles of the brain. Region 408 shows decreasedfunction in the frontal section.

Turning now to FIG. 5, a positron emissions tomography brain scan havingregions of interest is shown in accordance with an illustrativeembodiment. Scan 500 is a magnetic resonance imaging scan of a patient'sbrain. Scan 502 is a magnetic resonance imaging scan of a normal,healthy subject's brain. Scan 500 includes region of interest 504.Region 504 shows an abnormal enlargement of the ventricles of the brainwhen compared with scan 502 of a normal, healthy subject. Theenlargement of the brain ventricles shown in region of interest 504 isan indicator of a neuropsychiatric condition, such as, for example andwithout limitation, schizophrenia. Therefore, a neuroimage mappingmanager identifies region 504 as a region of interest.

FIG. 6 is a flowchart illustrating a process for analyzing a set ofbrain scans for a patient to identify regions of interest with links torelevant portions of the medical literature in accordance with anillustrative embodiment. The process in FIG. 6 may be implemented bysoftware for analyzing patient brain scans to identify regions ofinterest in the brain scans and generate links to portions of interestin the medical literature, such as neuroimage mapping manager 300 inFIG. 3.

The neuroimage mapping manager receives a set of scans for a patient(step 602). The set of scans may include functional magnetic resonanceimaging (fMRI) scans, structural magnetic resonance imaging (sMRI)scans, positron emission tomography (PET) scans, or any other type ofbrain scans. The neuroimage mapping manager analyzes the set of scans toidentify regions of interest in the scans based on baseline normal scansand/or baseline abnormal scans for identified disorders (step 604). Theneuroimage mapping manager displays the identified regions of interestto a user (step 606). The neuroimage mapping manager makes adetermination as to whether a selection of one or more additionalregions of interest is received from the user (step 608).

If a selection of one or more additional regions of interest is receivedfrom the user, the neuroimage mapping manager adds the one or moreselected regions to the regions of interest (step 610). After adding theselected regions to the regions of interest at step 610 or if noselections of additional regions are received from the user at step 608,the neuroimage mapping manager retrieves relevant medical literaturefrom a set of sources using search engines, pattern recognition,queries, and/or data mining (step 612). The embodiments are not limitedto using only search engines, queries, and data mining. Any known oravailable method for locating desired information in an electronic datasource may be utilized.

Next, the neuroimage mapping manager identifies portions of interest inthe medical literature associated with and/or describing the regions ofinterest (step 614). The portions of interest may include pages,paragraphs, or portions of text describing one or more of the regions ofinterest, the appearance of one or more of the regions of interest, orthe characteristics of one or more of the regions of interest. Theportions of interest in the relevant medical literature may includeimages of scans containing one or more of the regions of interest,portions of text in the medical literature describing diseases,deficiencies, illnesses, and/or abnormalities that may cause theappearance of one or more of the regions of interest or one or morecharacteristics of the regions of interest, or any other portion ofmedical literature that is relevant to one or more of the regions ofinterest in the patient's scans. The neuroimage mapping manager outputsresults identifying the regions of interest with a set of links to theportions of interest in the medical literature (step 616) with theprocess terminating thereafter.

In this embodiment, the regions of interest are displayed to the userand the user is given an opportunity to select one or more additionalregions of interest to add to the regions of interest identified by theneuroimage mapping manager. In another embodiment, the regions ofinterest are not presented to the user prior to identifying the portionsof interest in the medical literature. In this embodiment, the user isnot required to review the regions of interest and provide input as towhether to add one or more additional regions of interest. In this case,the process may occur completely automatically without any user inputduring the process of analyzing the patient's scans to identify regionsof interest and linking portions of the relevant medical literature tothe regions of interest.

Referring now to FIG. 7, a flowchart illustrating a process forgenerating baseline control scans is shown in accordance with anillustrative embodiment. The process in FIG. 7 may be implemented bysoftware for generating a set of baseline control scans, such as medicaldata and text analytics 314 in FIG. 3.

The process begins by searching a set of medical literature sources forscans of normal, healthy subjects (step 702). The scans of the normal,healthy subjects are saved in a data storage device to form baselinenormal scans (step 704). The process searches the set of medicalliterature sources for scans of subjects having known conditions (step706). The conditions may be a disease, an illness, an infection, adeformity, or any other condition. The scans of the subjects having theknown conditions are saved in the data storage device to form baselineabnormal scans (step 708) with the process terminating thereafter.

FIG. 8 is a flowchart illustrating a process for automaticallygenerating a treatment plan in accordance with an illustrativeembodiment. The process in FIG. 8 is implemented by software forautomatically generating treatment plans based on neuroimage data, suchas, treatment plan generator 348 in FIG. 3.

The process begins by retrieving a current set of diagnoses for apatient (step 802). The treatment plan generator analyzes relevantportions of the medical literature for potential therapies to treat eachcondition identified in the set of diagnoses and the treatment plangenerator identifies the potential therapies (step 804). The treatmentplan generator selects a set of recommended therapies based on themedical literature (step 805). The treatment plan generator identifiesdrug interactions, side effects of therapies in the set of recommendedtherapies, risks associated with the therapies in the set of recommendedtherapies based on pre-existing conditions, allergies, andcontraindications for the therapies in the set of recommended therapies(step 806). The treatment plan generator eliminates therapies from theset of recommended therapies (step 808). The treatment plan generatorgenerates a recommended treatment plan for each condition identified inthe current set of diagnoses (step 810) with the process terminatingthereafter.

Therefore, in one embodiment, a computer implemented method, apparatus,and computer program product is provided for developing neuropsychiatrictreatment plans. A treatment plan generator receives a set of diagnosesfor a patient. The treatment plan generator automatically analyzesmedical information in a set of electronic medical literature sourcesfor potential therapies associated with treatment of each identifiedcondition in the set of diagnoses. The treatment plan generatoridentifies the potential therapies associated with the treatment of eachdiagnosed condition. The treatment plan generator selects a set ofrecommended therapies from the potential therapies based on portions ofthe medical literature describing each therapy in the potentialtherapies and a medical history for the patient. The treatment plangenerator generates a treatment plan. The treatment plan comprises theset of recommended therapies to treat each diagnosed condition in theset of diagnoses.

Thus, the treatment plan generator automatically identifies therapiesfor one or more neuropsychiatric conditions and generates a treatmentplan for applying the identified therapies to a patient withoutrequiring input or intervention from a human user. The treatment plangenerator lessens the workload on physicians and researchers, permitsmore accurate data interpretation and analysis of scans, and allowsphysicians and researchers to more quickly establish treatment plans forpatients suffering from neuropsychiatric conditions.

In addition, the treatment plan generator provides a decision supporttool for clinicians in both clinical and research settings, to help themidentify treatments for neuropsychiatric conditions in complex cases, aswell as to determine whether a therapy, such as talk therapy,pharmacotherapy, or mechanical electroconvulsive therapy, will bebeneficial in light of drug interactions, side effects, allergies,medical history, pre-existing conditions, neuroimage data, andinformation available in the medical literature. Moreover, the treatmentplan generator maps relevant portions of the medical literature onto apatient's treatment plan for reference prior to, during, and afterapplication of each therapy in the treatment plan. In addition, ratherthan requiring medical personnel and pharmacy employees to manuallysearch for contraindications, the treatment plan generator automatesresearch of contraindications, side effects, and other risks associatedwith treatments and includes that information in the treatment plan forthe patient.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof code, which comprises one or more executable instructions forimplementing the specified logical function(s). It should also be notedthat, in some alternative implementations, the functions noted in theblock may occur out of the order noted in the figures. For example, twoblocks shown in succession may, in fact, be executed substantiallyconcurrently, or the blocks may sometimes be executed in the reverseorder, depending upon the functionality involved. It will also be notedthat each block of the block diagrams and/or flowchart illustration, andcombinations of blocks in the block diagrams and/or flowchartillustration, can be implemented by special purpose hardware-basedsystems that perform the specified functions or acts, or combinations ofspecial purpose hardware and computer instructions.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of the invention. Asused herein, the singular forms “a”, “an” and “the” are intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprises”and/or “comprising,” when used in this specification, specify thepresence of stated features, integers, steps, operations, elements,and/or components, but do not preclude the presence or addition of oneor more other features, integers, steps, operations, elements,components, and/or groups thereof.

The corresponding structures, materials, acts, and equivalents of allmeans or step plus function elements in the claims below are intended toinclude any structure, material, or act for performing the function incombination with other claimed elements as specifically claimed. Thedescription of the present invention has been presented for purposes ofillustration and description, but is not intended to be exhaustive orlimited to the invention in the form disclosed. Many modifications andvariations will be apparent to those of ordinary skill in the artwithout departing from the scope and spirit of the invention. Theembodiment was chosen and described in order to best explain theprinciples of the invention and the practical application, and to enableothers of ordinary skill in the art to understand the invention forvarious embodiments with various modifications as are suited to theparticular use contemplated.

The invention can take the form of an entirely hardware embodiment, anentirely software embodiment or an embodiment containing both hardwareand software elements. In a preferred embodiment, the invention isimplemented in software, which includes but is not limited to firmware,resident software, microcode, etc.

Furthermore, the invention can take the form of a computer programproduct accessible from a computer-usable or computer-readable mediumproviding program code for use by or in connection with a computer orany instruction execution system. For the purposes of this description,a computer-usable or computer readable medium can be any tangibleapparatus that can contain, store, communicate, propagate, or transportthe program for use by or in connection with the instruction executionsystem, apparatus, or device.

The medium can be an electronic, magnetic, optical, electromagnetic,infrared, or semiconductor system (or apparatus or device) or apropagation medium. Examples of a computer-readable medium include asemiconductor or solid state memory, magnetic tape, a removable computerdiskette, a random access memory (RAM), a read-only memory (ROM), arigid magnetic disk and an optical disk. Current examples of opticaldisks include compact disk-read only memory (CD-ROM), compactdisk-read/write (CD-R/W) and DVD.

A data processing system suitable for storing and/or executing programcode will include at least one processor coupled directly or indirectlyto memory elements through a system bus. The memory elements can includelocal memory employed during actual execution of the program code, bulkstorage, and cache memories which provide temporary storage of at leastsome program code in order to reduce the number of times code must beretrieved from bulk storage during execution.

Input/output or I/O devices (including but not limited to keyboards,displays, pointing devices, etc.) can be coupled to the system eitherdirectly or through intervening I/O controllers.

Network adapters may also be coupled to the system to enable the dataprocessing system to become coupled to other data processing systems orremote printers or storage devices through intervening private or publicnetworks. Modems, cable modem and Ethernet cards are just a few of thecurrently available types of network adapters.

The description of the present invention has been presented for purposesof illustration and description, and is not intended to be exhaustive orlimited to the invention in the form disclosed. Many modifications andvariations will be apparent to those of ordinary skill in the art. Theembodiment was chosen and described in order to best explain theprinciples of the invention, the practical application, and to enableothers of ordinary skill in the art to understand the invention forvarious embodiments with various modifications as are suited to theparticular use contemplated.

1. A method of developing neuropsychiatric treatment plans, the methodcomprising: a computer receiving a set of scans for a patient; thecomputer automatically analyzing the set of scans to identify regions ofinterest; the computer identifying brain architecture and brainmetabolism based on characteristics in the regions of interest; and thecomputer generating a set of diagnoses based on the characteristics inthe regions of interest, the brain architecture, and the brainmetabolism; the computer receiving the set of diagnoses for the patient,wherein the set of diagnoses comprises diagnoses for neuropsychiatricconditions; the computer automatically analyzing medical information ina set of electronic medical literature sources for potential therapiesassociated with treatment of each identified condition in the set ofdiagnoses; the computer identifying the potential therapies associatedwith the treatment of each diagnosed condition; the computer selecting aset of recommended therapies from the potential therapies based onportions of the medical literature describing each therapy in thepotential therapies and a medical history for the patient, wherein theset of recommended therapies comprises a plurality of therapiesrecommended for treatment of each neuropsychiatric condition identifiedin the set of diagnoses; the computer generating a treatment plan,wherein the treatment plan comprises the set of recommended therapies totreat each condition identified in the set of diagnoses; and thecomputer presenting the treatment plan to a user with a link to relevantportions of the medical literature associated with each therapy in thetreatment plan.
 2. The method of claim 1 wherein the treatment planfurther comprises an identification of negative drug interactions, sideeffects, allergic reactions, and negative effects on pre-existingconditions of the patient that are associated with the set ofrecommended therapies.
 3. The method of claim 1 wherein selecting theset of recommended therapies from the potential therapies based onportions of the medical literature describing each therapy in thepotential therapies and a medical history for the patient furthercomprises: responsive to identifying a therapy in the potentialtherapies that cannot be applied in conjunction with at least one othertherapy in the potential therapies to form an inapplicable therapy, thecomputer removing the inapplicable therapy from the set of potentialtherapies.
 4. The method of claim 1 wherein selecting the set ofrecommended therapies from the potential therapies based on portions ofthe medical literature describing each therapy in the potentialtherapies and a medical history for the patient further comprises: thecomputer receiving the medical history, wherein the medical historycomprises an identification of therapies contraindicated for thepatient, wherein a contraindicated substance is a substance with apotential to cause an exacerbation of an underlying condition in thepatient; and responsive to identifying a therapy associated with acontraindication, the computer removing the therapy from the set ofrecommended therapies.
 5. The method of claim 1 wherein selecting theset of recommended therapies from the potential therapies based onportions of the medical literature describing each therapy in thepotential therapies and a medical history for the patient furthercomprises: the computer receiving information describing druginteractions and a list of drugs currently being taken by the patient;and responsive to identifying a therapy in the potential therapies thatincludes utilization of a drug that produces a negative drug interactionwith a drug on the list of drugs currently being taken by the patient,the computer automatically removing the therapy from the set ofrecommended therapies.
 6. The method of claim 1 wherein selecting theset of recommended therapies from the potential therapies based onportions of the medical literature describing each therapy in thepotential therapies and the medical history for the patient furthercomprises: the computer receiving the medical history, wherein themedical history comprises an identification of pre-existing conditionsof the patient; and responsive to identifying a therapy identified as acause of a negative impact on a pre-existing condition of the patient,the computer automatically removing the therapy from the set ofrecommended therapies.
 7. The method of claim 1 further comprising: thecomputer presenting the treatment plan with relevant portions of themedical literature associated with each therapy in the treatment plan,wherein the relevant portions of the medical literature comprisesinformation describing drug interaction warning, side-effects associatedwith each therapy, dosage recommendations, recommendations on afrequency for administering each therapy to the patient,contraindications, allergy warnings, and an expected response to eachtherapy by the patient.
 8. A computer program product for developingneuropsychiatric treatment plans, the computer program productcomprising: one or more computer-readable, tangible storage devices;program instructions, stored on at least one of the one or more storagedevices, to receive a set of scans for a patient; program instructions,stored on at least one of the one or more storage devices, toautomatically analyze the set of scans to identify regions of interest;program instructions, stored on at least one of the one or more storagedevices, to identify brain architecture and brain metabolism based oncharacteristics in the regions of interest; and program instructions,stored on at least one of the one or more storage devices, to generate aset of diagnoses based on the characteristics in the regions ofinterest, the brain architecture, and the brain metabolism; programinstructions, stored on at least one of the one or more storage devices,to receive the set of diagnoses for the patient, wherein the set ofdiagnoses comprises diagnoses for neuropsychiatric conditions; programinstructions, stored on at least one of the one or more storage devices,to automatically analyze medical information in a set of electronicmedical literature sources for potential therapies associated withtreatment of each identified condition in the set of diagnoses; programinstructions, stored on at least one of the one or more storage devices,to identify the potential therapies associated with the treatment ofeach diagnosed condition; program instructions, stored on at least oneof the one or more storage devices, to select a set of recommendedtherapies from the potential therapies based on portions of the medicalliterature describing each therapy in the potential therapies and amedical history for the patient, wherein the set of recommendedtherapies comprises a plurality of therapies recommended for treatmentof each neuropsychiatric condition identified in the set of diagnoses;program instructions, stored on at least one of the one or more storagedevices, to generate a treatment plan, wherein the treatment plancomprises the set of recommended therapies to treat each diagnosedcondition in the set of diagnoses; and program instructions, stored onat least one of the one or more storage devices, to present thetreatment plan to a user with a link to relevant portions of the medicalliterature associated with each therapy in the treatment plan.
 9. Thecomputer program product of claim 8 wherein the treatment plan furthercomprises an identification of negative drug interactions, side effects,allergic reactions, and negative effects on pre-existing conditions ofthe patient that are associated with the set of recommended therapies.10. The computer program product of claim 8 further comprising: programinstructions, stored on at least one of the one or more storage devices,to identify a therapy in the potential therapies that cannot be appliedin conjunction with at least one other therapy in the potentialtherapies to form an inapplicable therapy and remove the inapplicabletherapy from the set of potential therapies.
 11. The computer programproduct of claim 8 further comprising: program instructions, stored onat least one of the one or more storage devices, to receive the medicalhistory, wherein the medical history comprises an identification oftherapies contraindicated for the patient, wherein a contraindicatedsubstance is a substance with a potential to cause an exacerbation of anunderlying condition in the patient; and program instructions, stored onat least one of the one or more storage devices, to remove the therapyfrom the set of therapies in response to identifying the therapy as atherapy associated with a contraindicated substance.
 12. The computerprogram product of claim 8 further comprising: program instructions,stored on at least one of the one or more storage devices, to receiveinformation describing drug interactions and a list of drugs currentlybeing taken by the patient; and program instructions, stored on at leastone of the one or more storage devices, to identify a therapy in thepotential therapies that includes utilization of a drug that produces anegative drug interaction with a drug on the list of drugs currentlybeing taken by the patient and automatically remove the therapy from theset of recommended therapies.
 13. The computer program product of claim8 further comprising: program instructions, stored on at least one ofthe one or more storage devices, to receive the medical history, whereinthe medical history comprises an identification of pre-existingconditions of the patient; and program instructions, stored on at leastone of the one or more storage devices, to identify a therapy identifiedas a cause of a negative impact on a pre-existing condition of thepatient and automatically removing the therapy from the set ofrecommended therapies.
 14. A computer system for developingneuropsychiatric treatment plans, the computer system comprising: one ormore processors, one or more computer-readable memories and one or morecomputer-readable, tangible storage devices; program instructions,stored on at least one of the one or more storage devices for executionby at least one of the one or more processors via at least one of theone or more memories, to receive a set of scans for a patient; programinstructions, stored on at least one of the one or more storage devicesfor execution by at least one of the one or more processors via at leastone of the one or more memories, to automatically analyze the set ofscans to identify regions of interest; program instructions, stored onat least one of the one or more storage devices for execution by atleast one of the one or more processors via at least one of the one ormore memories, to identify brain architecture and brain metabolism basedon characteristics in the regions of interest; and program instructions,stored on at least one of the one or more storage devices for executionby at least one of the one or more processors via at least one of theone or more memories, to generate a set of diagnoses based on thecharacteristics in the regions of interest, the brain architecture, andthe brain metabolism; program instructions for execution by at least oneof the one or more processors via at least one of the one or morememories, stored on at least one of the one or more storage devices, toreceive the set of diagnoses for the patient, wherein the set ofdiagnoses comprises diagnoses for neuropsychiatric conditions; programinstructions, stored on at least one of the one or more storage devicesfor execution by at least one of the one or more processors via at leastone of the one or more memories, to automatically analyze medicalinformation in a set of electronic medical literature sources forpotential therapies associated with treatment of each identifiedcondition in the set of diagnoses; program instructions, stored on atleast one of the one or more storage devices for execution by at leastone of the one or more processors via at least one of the one or morememories, to identify the potential therapies associated with thetreatment of each diagnosed condition; program instructions, stored onat least one of the one or more storage devices for execution by atleast one of the one or more processors via at least one of the one ormore memories, to select a set of recommended therapies from thepotential therapies based on portions of the medical literaturedescribing each therapy in the potential therapies and a medical historyfor the patient, wherein the set of recommended therapies comprises aplurality of therapies recommended for treatment of eachneuropsychiatric condition identified in the set of diagnoses; andprogram instructions, stored on at least one of the one or more storagedevices for execution by at least one of the one or more processors viaat least one of the one or more memories, to generate a treatment plan,wherein the treatment plan comprises the set of recommended therapies totreat each diagnosed condition in the set of diagnoses.
 15. The computersystem of claim 14 further comprising: program instructions, stored onat least one of the one or more storage devices for execution by atleast one of the one or more processors via at least one of the one ormore memories, to present the treatment plan to a user with a link torelevant portions of the medical literature associated with each therapyin the treatment plan.
 16. The computer system of claim 14 furthercomprising: program instructions, stored on at least one of the one ormore storage devices for execution by at least one of the one or moreprocessors via at least one of the one or more memories, to receiveinformation describing drug interactions and a list of drugs currentlybeing taken by the patient; identifying a therapy in the potentialtherapies that includes utilization of a drug that produces a negativedrug interaction with a drug on the list of drugs currently being takenby the patient; and automatically remove the therapy from the set ofrecommended therapies.
 17. The computer system of claim 14 furthercomprising: program instructions, stored on at least one of the one ormore storage devices for execution by at least one of the one or moreprocessors via at least one of the one or more memories, to receive themedical history, wherein the medical history comprises an identificationof pre-existing conditions of the patient; identify a therapy identifiedas a cause of a negative impact on a pre-existing condition of thepatient; and automatically remove the therapy from the set ofrecommended therapies.
 18. A data processing system for developingneuropsychiatric treatment plans, the system comprising: a scanningdevice, wherein the scanning device generates a set of scans for apatient; a neuroimage analyzer, wherein the neuroimage analyzer analyzesthe set of scans for the patient to identify a set of regions ofinterest in the scans, wherein a region of interest is an area thatshows an indicator of a neuropsychiatric condition; a diagnostic engine,wherein the diagnostic engine generates a set of diagnoses based on ananalysis of indicators identified in the set of regions of interest andrelevant portions of medical literature, wherein the set of diagnosescomprises diagnoses for neuropsychiatric conditions; a data storagedevice, wherein the data storage device stores the set of diagnoses forthe patient; a set of electronic medical literature sources, wherein theset of electronic medical literature sources comprises medicalliterature in an electronic form; a treatment plan generator, whereinthe treatment plan generator automatically analyzes the medicalliterature in the set of electronic medical literature sources forpotential therapies associated with treatment of each identifiedcondition in the set of diagnoses; identifies the potential therapiesfor treatment of each diagnosed condition; selects a set of recommendedtherapies from the potential therapies based on portions of the medicalliterature describing each therapy in the potential therapies and amedical history for the patient, wherein the set of recommendedtherapies comprises a plurality of therapies recommended for treatmentof each neuropsychiatric condition identified in the set of diagnoses;generates a treatment plan, wherein the treatment plan comprises the setof recommended therapies to treat each diagnosed condition in the set ofdiagnoses; and presents the treatment plan to a user with a link torelevant portions of the medical literature associated with each therapyin the treatment plan.